MNT Add validation for parameter alphas in LassoCV #22305
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added 4 commits
January 26, 2022 15:30
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jjerphan
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LGTM. Here are minor suggestions.
This remarks is out of the scope of this PR: to only perform validation once, might we want to split the current LassoCV.fit into a (LassoCV.fit, LassoCV._fit) couple, where LassoCV.fit would be the "front-end" performing the validation, and LassoCV._fit would be called internally, potentially several times?
Co-authored-by: Julien Jerphanion <[email protected]>
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thomasjpfan
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Feb 25, 2022
Co-authored-by: Thomas J. Fan <[email protected]>
thomasjpfan
reviewed
Feb 25, 2022
Co-authored-by: Thomas J. Fan <[email protected]>
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Thanks for your comments, @jjerphan and @thomasjpfan ! |
thomasjpfan
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Feb 26, 2022
LassoCV
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Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]>
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Reference Issues/PRs
Solves a part of the Issues #20724 and #21927. See also PR #21606.
What does this implement/fix? Explain your changes.
Use the helper function
check_scalarfromsklearn.utilsto validate the scalar parameteralphasand make sure to get consistent error types and messages inlinear_model.LassoCV.In the case that
alphasis a list, anp.ndarrayor a tuple, the check iterates through the elements and gives feedback on the position of the first parameter that fails the check.Any other comments?
This PR is a first approach to implement the validation for the scalar parameter alphas. Making the process more efficient when
fitis called several times (large number of folds in the cv or large number of points in a grid search) will be addressed in a future PR.